This is my 50 Claude Code tips from 6 months of daily use personally and at Meta as a Staff Software Engineer. I've been coding with Claude Code basically 12 hours a day really trying to understand what makes Claude Code tik. Here's everything I wish I knew when I started, from foundations to advanced parallel workflows.

⏱️ TIMESTAMPS
0:00 - Intro
1:04 - ACT 1: Foundations (Tips 1-25)
1:18 - Tip 1: Run from root directory
1:56 - Tip 2: Run /init immediately
2:54 - Tip 3: CLAUDE.md is hierarchical
3:27 - Tip 4: Keep CLAUDE.md concise
3:58 - Tip 5: Structure: What, Domain, Validation
5:36 - Keyboard Shortcuts
5:58 - Tip 6: Shift+Tab toggles modes
6:40 - Tip 7: Escape interrupts
7:43 - Tip 8: Double Escape clears input
7:59 - Tip 9: Double Escape on empty = rewind
8:29 - Tip 10: Screenshot and drag
8:44 - Tip 11: Add context to screenshots
9:09 - Essential Commands
9:41 - Tip 12: /clear resets context
10:13 - Tip 13: /context shows token usage
11:42 - Tip 14: Let auto-compaction work
12:23 - Tip 15: /model switches models
12:49 - Tip 16: /resume recovers sessions
13:21 - Tip 17: /mcp shows MCP status
14:19 - Tip 18: /help shows all commands
14:33 - Tip 19: Git is your safety net
15:24 - CLAUDE.md Deep Dive
15:52 - Tip 20: Add a Critical Rules section
17:08 - Tip 21: Ask Claude to update rules
17:46 - Tip 22: Use workflow triggers
18:27 - Tip 23: Commit CLAUDE.md to git
19:34 - Tip 24: dangerously-skip for throwaway envs
20:39 - Tip 25: Combine skip with allowlists
20:59 - ACT 2: Daily Workflow (Tips 26-32)
21:38 - Tip 26: Start features in Plan Mode
23:46 - Tip 27: Fresh context beats bloated
24:29 - Tip 28: Persist before ending sessions
25:03 - Tip 29: Lazy load context
26:09 - Tip 30: Give verification commands
27:32 - Tip 31: Consider Opus for complex work
28:18 - Tip 32: Read thinking blocks
29:01 - ACT 3: Power User (Tips 33-40)
29:34 - Tip 33: Four composability primitives
29:54 - Tip 34: Skills = recurring workflows
31:33 - Tip 35: Commands = quick shorthand
32:18 - Tip 36: Never create commands manually
33:02 - Tip 37: MCPs = external service docs
33:52 - Tip 38: Ask Claude to install MCPs
34:15 - Tip 39: Subagents = isolated context
37:10 - Tip 40: Avoid instruction overload
37:48 - ACT 4: Advanced (Tips 41-50)
38:02 - Tip 41: Run multiple instances
39:06 - Tip 42: iTerm split panes
40:33 - Tip 43: Enable notifications
41:10 - Tip 44: Git worktrees for isolation
41:40 - Tip 45: /chrome connects browser
43:17 - Tip 46: Powerful for debugging
43:28 - Hooks & Automation
43:41 - Tip 47: Hooks intercept actions
44:10 - Tip 48: Auto-format with PostToolUse
44:24 - Tip 49: Block dangerous commands
44:43 - Tip 50: Explore the plugin ecosystem
45:32 - Context is King (Outro)

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Xiaoqing Ge, Senior Innovation Engineer at Baker Hughes, discusses the following:
• Overview of computer vision
• How deep learning works in computer vision?
• Traditional methods vs deep learning
• Survey of Baker Hughes applications
• Looking ahead

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In this video, I break down a complete AI agent tutorial for beginners in 2026, explaining what is an AI agent and how it works. You’ll learn how to build an AI agent step by step, including how to build an AI agent for free using modern tools and workflows, plus how AI automation fits into building smarter systems. Whether you're looking for a practical AI agent course or want to understand how to make an AI agent for free, this guide covers everything you need to get started.

00:00 - Intro: Why AI Agents are the Future of Productivity
00:54 - Personal Assistant: Monitoring Email & WhatsApp Updates
01:33 - Getting Started: Creating Your Free Base44 Account
02:21 - Dashboard Tour: Understanding Apps vs. Superagents
03:16 - The Employee Mindset: Setting Up Background AI Work
03:31 - Building Phase: Creating Your First AI Superagent
04:13 - Prompting Mastery: Building an Email Monitoring Agent
04:59 - Connectivity: Authorizing Gmail & Google Permissions
05:50 - The Brain Tab: Controlling Your Agent’s Identity & Personality
07:39 - The Soul: Defining Decision-Making Rules for AI
08:48 - Knowledge Base: Uploading Documents & Reference Files
09:45 - Memory Systems: How Your Agent Builds Context Over Time
10:45 - Integrations Hub: Connecting Slack, Calendar & Drive
12:05 - Chat Interface: Communicating with Your AI Assistant
13:30 - Voice Interaction: Using the Microphone Command Feature
14:37 - Tasks & Automation: Scheduled vs. Event-Triggered Workflows
16:13 - External Tools: Mastering OAuth & API Connections
18:47 - Security: Managing Secrets and API Keys Safely
19:28 - WhatsApp Integration: Chatting with AI from Your Phone
21:18 - API Access: Connecting Your Agent to External Systems
22:16 - Monetization: Setting Up Stripe & Payment Integration
23:19 - Real-World Flow: Automated Email Response Drafting
24:44 - Slack Optimization: Summarizing Busy Workspace Activity
26:00 - Reporting: Generating Automated Daily Activity Summaries
27:21 - Business Use Case: Customer Support Automation
28:19 - Prompt Frameworks: Writing Instructions That Get Results
29:44 - Credit Management: Understanding Usage and Costs
30:24 - Troubleshooting: Fixing Active Tasks & Connections
31:10 - Outro: Future-Proofing Your AI-Powered Business

For inquiries: Mikey (at) ytmedia.group

High Quality HDR 8K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 12k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

⭐ Note: To view at 8K 60P you will need to use Chrome & opera.

⭐ All Videos was shot, edited & color graded by me.
THESE 8K VIDEOS ARE FOR TV'S FOR DEMO AND TEST THE QUALITY OF ALL 8K TV'S.

⭐ I have done High color correction, Color changing, Raw Videos editing, HDR color Setting, merge files & 8K Export file etc.

Editing Video by
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This video created for entertainment informative, educational purposes and Film & Animation.


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Copyright
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⭐ Video Footage Copyright Under License.

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#12KHDR #DolbyVision #60fpsHigh Quality HDR 8K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 12k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

⭐ Note: To view at 8K 60P you will need to use Chrome & opera.

⭐ All Videos was shot, edited & color graded by me.
THESE 8K VIDEOS ARE FOR TV'S FOR DEMO AND TEST THE QUALITY OF ALL 8K TV'S.

⭐ I have done High color correction, Color changing, Raw Videos editing, HDR color Setting, merge files & 8K Export file etc.

Editing Video by
PREETI NAAGAR

This video created for entertainment informative, educational purposes and Film & Animation.

Editing Software
Adobe premiere pro

ALL CREDIT GOES TO YOUTUBE

Copyright
⭐ All The footage Was Edited And Color Corrected By Me.
⭐ Video Footage Copyright Under License.

All other rights reserved.

#12Khdr #DolbyVision #Animals&Nature

Watch highlights from RED and NVIDIA’s Dec. 12, 2018, event where they jointly announced upcoming availability of NVIDIA CUDA-accelerated REDCODE RAW decode SDK and REDCINE-X, giving software developers and studios a powerful new way to work with 5K, 6K or 8K video. Developers interested in participating in the private beta can contact [email protected]. Public beta begins January 2019.

Training a large-scale deep net is a computationally expensive process, and common CPUs are generally insufficient for the task. GPUs are a great tool for speeding up training, but there are several other options available.

Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv

A CPU is a versatile tool than can be used across many domains of computation. However, the cost of this versatility is the dependence on sophisticated control mechanisms needed to manage the flow of tasks. CPUs also perform tasks serially, requiring the use of a limited number of cores in order to build in parallelism. Even though CPU speeds and memory limits have increased over the years, a CPU is still an impractical choice for training large deep nets.

Vector implementations can be used to speed up the deep net training process. Generally, parallelism comes in the form of both parallel processing and parallel programming. Parallel processing can either involve shared resources on a single computer, or distributed computing across a cluster of nodes.

The GPU is a common tool for parallel processing. As opposed to a CPU, GPUs tend to hold large numbers of cores – anywhere from 100s to even 1000s. Each of these cores is capable of general purpose computing, and the core structure allows for large amounts of parallelism. As a result, GPUs are a popular choice for training large deep nets. The Deep Learning community provides GPU support through various libraries, implementations, and a vibrant ecosystem fostered by nVidia. The main downside of a GPU is the amount of power required to run one relative to the alternatives.

The “Field Programmable Gate Array”, or FPGA, is another choice for training a deep net. FPGAs were originally used by electrical engineers to design mock-ups for different computer chips without having to custom build a chip for each solution. With an FPGA, chip function can be programmed at the lowest level – the logic gate. With this flexibility, an FPGA can be tailored for deep nets so as to require less power than a GPU. Aside from speeding up the training process, FPGAs can also be used to run the resultant models. For example, FPGAs would be useful for running a complex convolutional net over thousands of images every second. The downside of an FPGA is the specialized knowledge required during design, setup, and configuration.

Another option is the “Application Specific Integrated Circuit”, or ASIC. ASICs are highly specialized, with designs built in at the hardware and integrated circuit level. Once built, they will perform very well at the task they were designed for, but are generally unusable in any other task. Compared to GPUs and FPGAs, ASICs tend to have the lowest power consumption requirements. There are several Deep Learning ASICs such as the Google Tensor Processing Unit (TPU), and the chip being built by Nervana Systems.

There are a few parallelism options available with distributed computing such as data parallelism, model parallelism, and pipeline parallelism. With data parallelism, different subsets of the data are trained on different nodes in parallel for each training pass, followed by parameter averaging and replacement across the cluster. Libraries like TensorFlow support model parallelism, where different portions of the model are trained on different devices in parallel. With pipeline parallelism, workers are dedicated to tasks, like in an assembly line. The main idea is to ensure that each worker is relatively well-utilized. A worker starts the next job as soon as the current one is complete, a strategy that minimizes the total amount of wasted time.

Parallel programming research has been active for decades, and many advanced techniques have been developed. Generally, algorithms should be designed with parallelism in mind in order to take full advantage of the hardware. One such way to do this is to decompose the data model into independent chunks that each perform one instance of a task. Another option is to group all the tasks by their dependencies, so that each group is completely independent of the others. As an addition, you can implement threads or processes that handle different task groups. These threads can be used as a standalone solution, but will provide significant speed improvements when combined with the grouping method. To learn more about this topic, follow this link to the Open HPI Massive Open Online course (MOOC) on parallel programming - https://open.hpi.de/courses/parprog2014.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Fine-tuning larger models can be tricky on consumer hardware. In this video I go over why its better to use large models for fine-tuning vs smaller models, I go over the issue with the naive approach to fine-tuning, and finally, I go over how to use DeepSpeed to successfully fine-tune even the largest GPT Neo model.

Notebook Git repo: https://github.com/mallorbc/GP....T_Neo_fine-tuning_no
finetuning repo: https://github.com/Xirider/finetune-gpt2xl
DeepSpeed repo: https://github.com/microsoft/DeepSpeed
happy transformers: https://happytransformer.com/
GPT article with images: https://towardsdatascience.com..../gpt-3-the-new-might

Timestamps

00:00 - Intro
00:36 - Background on fine-tuning
02:41 - Setting up Jupyter
05:17 - Incorrect naive fine-tuning method
10:02 - Correctly fine-tuning with DeepSpeed
15:35 - Fine-tuning the 2.7B model
16:35 - Fine-tuning the 1.3B model
17:55 - Looking at the README
20:08 - Outro and future work

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6 Reasons Why ChatGPT Can Make Your Business by simplilearn is a tutorial based in Artificial Inteliigence and Large Learning Models. This AI tutorial will discuss the top 6 reasons why chatGPT and AI can help you build a successful business.. The video cover How to Use ChatGPT to Grow Your Business and to tap out the best out of your business. This Simplilearn tutorial will cover the following six reasons.

00:00:00 Introduction to 6 Reasons Why chatGPT can make your business
00:02:00 Decision Making Capabilities
00:02:50 Customer Services
00:03:45 Content Creation
00:04:24 AI and ChatGPT for Data analysis
00:05:38 Sales and Marketing
00:06:49 Automation

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#ArtificalIntelligence #ChatGPT #MachineLearning #ChatGPTForBusiness #AI #ML #GPT3 #GPT4 #HowToUseChatGpt #ChatBotOpenAI #Simplilearn

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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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GPT-3 was in all news its Human-like Text Generation capabilities. Shame it was never opensourced by Open AI (!). Hence EleutherAI with a bunch of researchers set out to create a true GPT-3 open-source alternative and GPT-J-6B is the product of those efforts.

GPT-J-6B is a 6 billion parameter, autoregressive text generation model trained on The Pile.

Useful Links:
✅ Mesh Transformer JAX - https://github.com/kingoflolz/mesh-transformer-jax
✅ GPT-J-6B: 6B JAX-Based Transformer Blogpost by Aran Komatsuzaki - https://arankomatsuzaki.wordpr....ess.com/2021/06/04/g
✅Web Demo of GPT-J-6B for Text Generation - https://6b.eleuther.ai/
✅Colab (Python) Notebook - https://colab.research.google.....com/github/kingoflol
✅JAX - https://jax.readthedocs.io/en/....latest/notebooks/qui

Related Videos:

🎥 AI Text Generation with GPT-3 OpenSource Alternative GPT-Neo Model using Hugging Face Hub
- https://www.youtube.com/watch?v=0PuVk6c8Ua8

🎥 AI-Generated Blog Content with GPT-Neo (GPT-3 Alternative) + Gradio | Python ML Web App - https://youtu.be/d_xRYyy2LFM

Redux: Handle an Action in the Store


#100DaysOfCode

People have witnessed supernovae for millennia, but what threat do they pose to life on Earth? This video is sponsored by Brilliant. You can get started for free, or the first 200 people to sign up via https://brilliant.org/veritasium get 20% off a yearly subscription.

▀▀▀
A massive thanks to Prof. Hans-Thomas Janka for helping us with the physics of supernovae and GRBs. A massive thanks to Prof. Brian Thomas for all of his help with the terrestrial effects of supernovae and GRBs. This video would not have been possible without them. Also thanks to Dr. Luke Barnes for his initial help with the literature search.

Hydrogen bomb vs Supernova fact was taken from this great article by xkcd/Randall Munroe – https://what-if.xkcd.com/73/ (based on the calculation by Andrew Karam, 2002)

Cosmic bubble footage from
https://www.cfa.harvard.edu/ne....ws/1000-light-year-w

Neutrino driven SN explosion simulations from https://iopscience.iop.org/art....icle/10.1088/2041-82

▀▀▀
References:

Melott, A. et al. (2019). Hypothesis: Muon radiation dose and marine megafaunal extinction at the End-Pliocene supernova. Astrobiology, 19(6), 825-830. – https://ve42.co/Melott1

Thomas, B. C. et al. (2016). Terrestrial effects of nearby supernovae in the early Pleistocene. The Astrophysical Journal Letters, 826(1), L3 – https://ve42.co/Thomas1

Melott, A. L., & Thomas, B. C. (2019). From cosmic explosions to terrestrial fires?. The Journal of Geology, 127(4), 475-481. – https://ve42.co/Melott2

Fields, B. et al. (2019). Near-Earth supernova explosions: Evidence, implications, and opportunities. arXiv preprint arXiv:1903.04589. – https://ve42.co/Fields1

Thomas, B. C., Atri, D., & Melott, A. L. (2021). Gamma-ray bursts: not so much deadlier than we thought. Monthly Notices of the Royal Astronomical Society, 500(2), 1970-1973. – https://ve42.co/Thomas2

Melott, A. et al. (2004). Did a gamma-ray burst initiate the late Ordovician mass extinction?. International Journal of Astrobiology, 3(1), 55-61. – https://ve42.co/Melott3

Firestone, R. B. (2014). Observation of 23 supernovae that exploded less than 300 pc from Earth during the past 300 kyr. The Astrophysical Journal, 789(1), 29. – https://ve42.co/firestone1

Janka, H. T. (2017). Neutrino emission from supernovae. arXiv preprint arXiv:1702.08713. – https://ve42.co/Janka1

Janka, H. T., & Hillebrandt, W. (1989). Neutrino emission from type II supernovae-an analysis of the spectra. Astronomy and astrophysics, 224, 49-56. – https://ve42.co/Janka2

Janka, H. T. (2017). Neutrino-driven explosions. arXiv preprint arXiv:1702.08825. – https://ve42.co/Janka3

Karam, P. A. (2002). Gamma and neutrino radiation dose from gamma ray bursts and nearby supernovae. Health physics, 82(4), 491-499. – https://ve42.co/Karam1

Melott, A. L., Thomas, et al.. (2017). A supernova at 50 pc: effects on the Earth's atmosphere and biota. The Astrophysical Journal, 840(2), 105. – https://ve42.co/Melott4

Ludwig, P., et al. (2016). Time-resolved 2-million-year-old supernova activity discovered in Earth’s microfossil record. Proceedings of the National Academy of Sciences, 113(33), 9232-9237. – https://ve42.co/Ludwig1

Gritschneder, et al. (2011). The supernova triggered formation and enrichment of our solar system. The Astrophysical Journal, 745(1), 22. – https://ve42.co/Gritschneder1

Motizuki, Y., Takahashi, et al. (2009). An Antarctic ice core recording both supernovae and solar cycles. arXiv preprint arXiv:0902.3446. – https://ve42.co/Motizuki

Zucker, C. et al. (2022). Star formation near the Sun is driven by expansion of the Local Bubble. Nature, 601(7893), 334-337. – https://ve42.co/Zucker1

Hirata, K. et al.(1987). Observation of a neutrino burst from the supernova SN1987A. – https://ve42.co/Hirata1

Hayes, L. A., & Gallagher, P. T. (2022). A Significant Sudden Ionospheric Disturbance Associated with Gamma-Ray Burst GRB 221009A. Research Notes of the AAS, 6(10), 222.

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Written by Petr Lebedev & Derek Muller
Edited by Fabio Albertelli
Animation by Fabio Albertelli, Jakub Misiek, Alex Drakoulis, Ivy Tello, Mike Radjabov, and Charlie Davies
Filmed by Derek Muller
Additional Research by Kovi Rose & Katie Barnshaw
Video/photos supplied by NASA, ESA, Pond5, and Getty Images
Music from Epidemic Sound & Jonny Hyman
Produced by Derek Muller, Petr Lebedev, and Emily Zhang

Intellipaat JavaScript training: https://intellipaat.com/full-s....tack-web-developer-m
In this JavaScript Tutorial For Beginners video, You will learn about variables, data types, functions, arrays, event handling, form validation, etc. This is a must-watch JavaScript Course for beginners who want to learn JavaScript and make a career in it.

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JavaScript Tutorial TimeStamp:
00:00:00 - JavaScript Tutorial For Beginners
00:01:50 - What is JavaScript?
00:06:24 - How to use JavaScript?
00:09:00 - Hands-on: Print a message in the console
00:19:37 - What can you build with JavaScript?
00:31:06 - JavaScript Basics
00:34:22 - Variables
00:44:50 - JavaScript Data Types
00:54:01 - Operators
01:15:17 - Loops
01:26:50 - Arrays
01:39:58 - Objects
01:50:03 - JavaScript Functions
02:12:37 - OOP
02:23:20 - JSON
02:26:55 - ES6 Features
02:36:16 - Arrow Functions
02:52:00 - Hands-on: Class
02:55:06 - JavaScript and DOM
03:08:57 - JavaScript Events
03:15:25 - Asynchronous JavaScript
03:19:29 - Hands-on: Asynchronous JavaScript
03:28:01 - JavaScript and NodeJS
03:29:29 - Hands-on: NodeJS


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In this live session on Robot Framework Tutorial For Beginners, firstly we will learn automation
,Automation approaches
Basics of python
python unit testing
selenium WebDriver
read This video is must watch for everyone who wishes to learn Robot Framework With Python and make a career in it.

#RobotFrameworkTutorialForBeginners #RobotFrameworkWithPython #IntroductiontoRobotframework #Robotframework
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Following topics are covered in this session:
00:00 - Introduction to Robot Framework
02:30 - Course content
06:07- Benefits of Automation Testing
14:03- Why Automation
22:49- Automation Testing Process
31:04- Test Tool Selection
34:20- Framework For Automation
37: 32- Difference Between Manual and Automation Testing
41:49- Python Introduction
50:00- How To Install Python on Windows
56:25- Data Types and Variables
1:31:18- Looping Concepts
3:08:45- Polymorphism
3:31:48-Multiple Inheritance
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Sing's Best Songs - "Let me hear you sing!" Groove with all your favorite songs featuring Rosita (Reese Witherspoon), Meena (Tori Kelly), Gunter (Nick Kroll), Ash (Scarlett Johansson), Johnny (Taron Egerton), and more! What's your favorite song from the Sing movies?
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CREDITS:
TM & © Universal (2021)
Cast: Reese Witherspoon, Nick Kroll, Taron Egerton, Scarlett Johansson, Tori Kelly

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In this video, I walk through my complete workflow for tackling large coding projects using Claude Code's plan mode. I demonstrate how to start with a rough dictated prompt, use plan mode to explore the codebase and generate clarifying questions, and break complex work into multi-phase plans that can span multiple context windows. I show my custom rules configuration that keeps plans concise and adds unresolved questions, how to monitor context usage throughout implementation, and my strategy of storing plans as GitHub issues to preserve them across context resets. This approach combines upfront planning with aggressive auto-accept during implementation phases, allowing AI to handle substantial features while maintaining control and code quality. I share practical tips including my favorite concision rule, the benefits of multi-phase planning, and how to effectively manage context windows for large projects.

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Deep learning has shown remarkable success in processing and understanding unstructured data like text and images. In this module, we will explore how deep neural networks can be leveraged to build intelligent systems in the domains of natural language processing and computer vision. Get ready to dive into the exciting world of deep learning on text and images!

We will start with the agenda and course overview. Then, we will review some key machine learning concepts, including regression, optimization, regularization, and text and data representation.

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This video is part of Machine Learning University’s open-source "Application of Deep Learning to Text and Image Data" series, part of our Fundamentals of Machine Learning content. Access the full set of lessons, hands-on labs, and Jupyter Notebooks here:

🔗 GitHub Repository: https://github.com/aws-mlu/aws....-mlu-eep-traditional
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Machine Learning University provides free, open-source AI/ML educational content for educators. New content is released regularly — subscribe to stay updated.

🔥Digital Marketing Specialist - https://www.simplilearn.com/master-in-digital-marketing?utm_campaign=eDq9GHU84nQ&utm_medium=Lives&utm_source=Youtube
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This video on Search Engine Marketing Full Course 2026 by Simplilearn, explains how paid advertising works on search engines like Google. You’ll learn Google Ads basics, keyword targeting, ad creation, bidding strategies, and campaign optimization. The course also covers conversion tracking, performance analysis, and ROI-focused marketing. With real-world examples, you’ll understand how businesses drive traffic and sales using paid search. Perfect for beginners, marketers, and professionals looking to master performance marketing.

Related Videos:
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➡️ About Digital Marketing Specialist

Experience live and interactive learning with this comprehensive digital marketing masters program. Learn to leverage ChatGPT and other generative AI tools for digital marketing. Additionally, you will receive a Meta Certified Digital Marketing Associate exam in the program.

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✅ Key Features
✅ Industry recognised Digital Marketing Specialist certificate from Simplilearn
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✅ Learn 35+ digital marketing tools
✅ 5 Capstone problem statements and 15+ course-end projects
✅ 10+ case studies from brands like Adidas, KFC, Nike, Intel, etc.

Skills Covered:
✅ Website Creation
✅ Behavioral Marketing
✅ Keyword Research
✅ Search Engine Optimization
✅ Search Engine Marketing
✅ Campaign Management
✅ Social Media Marketing
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